rahasya

Thirty-four kinds of personal identifier, each a pattern with a checksum, read in context. 1.000 precision at recall 1.000 on a held-out corpus, 0.996 on the one they were tuned against. No model, no network, no gigabyte on disk.

Install

uv add rahasya

The one call

r = pii_report('Invoice for ada@example.com. Card 4111 1111 1111 1111. '
                'KEY=sk-abcdefghijklmnopqrstuvwxyz123456')
r.has_pii, r.identifying

has_pii counts only IDENTIFYING kinds. An IP address is reported and does not tip the gate.

redact('Card 4111 1111 1111 1111 charged to ada@example.com')

Digits are read in context

A number failing its checksum is not the thing the checksum protects. A number introduced by a reference word is not an identifier.

[pii_report(t).has_pii for t in
 ('Order 4111 1111 1111 1112 shipped',   # fails Luhn
  'Conforms to EN 60601-1',              # a standard, not a ZIP
  'Card 4111 1111 1111 1111')]           # this one is a card

DESIGNATOR holds the words that do it: ISO, RFC, invoice, page, commit, order and thirty more. It is matched against the 48 characters to the left of a candidate.

Nineteen regional identifiers, each with its own checksum

import random
a = gen_aadhaar(random.Random(0))            # a valid one, for a fixture
a, aadhaar_ok(a), pesel_ok('44051401359'), nino_ok('AB123456C')

Names are opt-in

ner=True turns on an honorific-anchored pass. Dr Charles Babbage matches; a bare Ada Lovelace does not. scanned_ner says whether anything looked, which keeps “none found” apart from “not looked for”.

(pii_report('Dr Charles Babbage signed it.').has_pii,
 pii_report('Dr Charles Babbage signed it.', ner=True).identifying,
 redact('Dr Charles Babbage signed it.', ner=True))

What is here

luhn, aadhaar_ok, pesel_ok and the rest are exported too, because a checksum is useful on its own.

No model

Two learned detectors were measured against these patterns. Both lose on precision and recall, and are 200 times slower. A DeBERTa-v3 ONNX classifier earns its gigabyte only on names no honorific introduces (2/8 to 5/8 of them). A 350M tflite encoder finds no names at all.

Development

The notebooks in nbs/ are the source; the modules are generated.

pip install -e .
nbdev_prepare